Suitability Analysis and Projected Climate Change Impact on Banana and Coffee Production Zones in Nepal

Abstract

The Government of Nepal has identified opportunities in agricultural commercialization, responding to a growing internal demand and expansion of export markets to reduce the immense trade deficit. Several cash crops, including coffee and bananas, have been identified in the recently approved Agriculture Development Strategy. Both of these crops have encouraged smallholder farmers to convert their subsistence farming practices to more commercial cultivation. Identification of suitable agro-ecological zones and understanding climate-related issues are important for improved production and livelihoods of smallholder farmers. Here, the suitability of coffee and banana crops is analyzed for different agro-ecological zones represented by Global Environmental Stratification (GEnS). Future shifts in these suitability zones are also predicted. Plantation sites in Nepal were geo-referenced and used as input in species distribution modelling. The multi-model ensemble model suggests that climate change will reduce the suitable growing area for coffee by about 72% across the selected emission scenarios from now to 2050. Impacts are low for banana growing, with a reduction in suitability by about 16% by 2050. Bananas show a lot of potential for playing an important role in Nepal as a sustainable crop in the context of climate change, as this study indicates that the amount of area suited to banana growing will grow by 40% by 2050. Based on our analysis we recommend possible new locations for coffee plantations and one method for mitigating climate change-related problems on existing plantations. These findings are expected to support planning and policy dialogue for mitigation and support better informed and scientifically based decision-making relating to these two crops.

Climate Changes and Their Impact on Agricultural Market Systems: Examples from Nepal

“…The WorldClim dataset's resolution of 1000 m is not able to reflect the surface accurately, especially in regions with steep slopes and narrow valleys, nor does it represent microclimatic situations [21]. Despite these limitations, the WorldClim dataset is the best-developed dataset available for Nepal [20,42,43,53,54]. Moreover, a study by Glotter et al [55] showed that, once a correction is applied, the results of general circulation models and regional climate models are indistinguishable in all four scenarios.…”

Climate Change, Carbon Dioxide, and Pest Biology, Managing the Future: Coffee as a Case Study

“…Projections for Puerto Rico are also dire; by 2071-2099, only 24 km 2 are projected to be suitable for coffee production [62]. Projected reductions in suitable coffee growing areas by 2050 in many other countries have also been reported [63][64][65].…”

Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages

“…A number of studies on species suitability account for univariate dissimilarity among covariates by restricting extrapolation to areas where predictors are within the reference range of individual covariates (Li et al 2016;Ranjitkar et al 2016). The multivariate environmental similarity surfaces (MESS;Elith et al 2010) is one of widely used algorithm to identify the areas in the projection domain where values of at least one individual covariate are beyond the reference range (Li et al 2016;Ranjitkar et al 2016). However, MESS is limited because it only accounts for the range of individual variables thus overlooking the multivariate combinations of covariates (Mesgaran et al 2014).…”

“…Bottom-up geospatial approach for delianating extrapolation domains involve a degree of extrapolation beyond the limits of observed covariate space due to spatial-temporal biases in trial site data and environmental heterogeneity (Elith et al 2010;Zurell et al 2012;Ranjitkar et al 2016). Two types of extrapolation can occur: (1) the range of values (minimum/maximum) for individual predictors in the projection domain are beyond that in the reference sites (Novelty type 1, NT1); or (2) predictors exhibit unique combinations (novel correlation structure) not observed in the reference domain (Novelty type 2, NT2) (Zurell et al 2012;Owens et al 2013;Mesgaran et al 2014).…”